An autoencoder-based snow drought index.

In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt.

Cover crop performance under a changing climate in continuous corn system over Nebraska.

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.

Impact of land use/cover change and slope gradient on soil organic carbon stock in Anjeni watershed, Northwest Ethiopia.

Today's agri-food systems face the triple challenge of addressing food security, adapting to climate change, and reducing the climate footprint by reducing the emission of greenhouse gases (GHG). In agri-food systems, changes in land use and land cover (LULC) could affect soil physicochemical properties, particularly soil organic carbon (SOC) stock. However, the impact varies depending on the physical, social, and economic conditions of a given region or watershed.

Explainable machine learning for the prediction and assessment of complex drought impacts.

Drought is a common and costly natural disaster with broad social, economic, and environmental impacts. Machine learning (ML) has been widely applied in scientific research because of its outstanding performance on predictive tasks. However, for practical applications like disaster monitoring and assessment, the cost of the models failure, especially false negative predictions, might significantly affect society.

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models.

Urban drought challenge to 2030 sustainable development goals.

In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs).

Upper Blue Nile Basin Water Budget from a Multi-Model Perspective.

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.

Ectoparasites Prevalence in Small Ruminants in and around Sekela, Amhara Regional State, Northwest Ethiopia.

This study was conducted to determine the prevalence and type of ectoparasites and to identify risk factors associated with ectoparasite infestations in small ruminants in and around Sekela, Northwest Ethiopia. Clinical examination and laboratory analysis were made on 304 sheep and 96 goats. The collected raw data were analyzed using χ (2)-test.